Machine Learning Engineer

Machine Learning Engineer San Francisco or remote Contract or full-time $80K to $150K 3+ years You will build and own production machine learning systems end to end, from data modeling and feature engineering to training, evaluation, deployment, and monitoring. The data is messy real-world healthcare data: claims, EHR, pharmacy, lab, ADT. The problems are ranking, prioritization, and prediction, where the model output drives a real human or autonomous workflow. WHAT YOU WILL DO Build and own production ML systems end to end: data modeling, feature engineering, training, evaluation, deployment, and monitoring. Design and implement data pipelines that turn raw, messy real-world healthcare data into reliable features. Train and evaluate models for ranking, prioritization, and prediction, for example identifying high-risk or high-priority members. Deploy models as reliable services or batch jobs, with clear versioning, monitoring, and rollback strategies. Make architectural decisions around model choice, evaluation metrics, retraining cadence, and system guardrails. Balance accuracy, explainability, reliability, and operational constraints. Collaborate directly with founders and engineers to translate product and operational needs into scalable, maintainable ML solutions. WHAT WE ARE LOOKING FOR At least 3 years building and deploying ML systems in production. Strong foundation in ML for structured (tabular) data: feature engineering, regression or classification models, ranking or prioritization. Experience with the full ML lifecycle: data prep, train/test, evaluation, deployment, retraining, monitoring. Solid backend engineering skills: production-quality code, services or batch jobs, databases, data pipelines. Good system design instincts. You understand trade-offs between model complexity, reliability, latency, and maintainability. Ability to clearly explain modeling choices, assumptions, and limitations to non-ML stakeholders. Bonus: healthcare or operational decision-support systems, LLMs in production (RAG, fine-tuning, structured prompting), model monitoring and data drift tooling. Apply for ML Engineer →